Benchmarking LMCache vs EdgeMatrix: Why Caching Alone Can’t Beat a Smarter Scheduler
In my previous article, “Redefining LLM Inference: How EdgeMatrix Outperforms vLLM and TensorRT-LLM”, I shared benchmark insights comparing leading inference
At the Core of the Edge: The Energy Performance in Edge AI Chips
“Energy is more problem to AI than compute.” Every AI chip can compute. Few can sustain. As intelligence moves from
Redefining LLM Inference: How EdgeMatrix Outperforms vLLM and TensorRT-LLM
As Large Language Models (LLMs) continue to evolve, the spotlight is shifting from model accuracy to how efficiently these models
Engineering Scalable Edge AI: The Semiconductor Stack Powering the Future
At SandLogic, we’ve built a complete AI acceleration stack ( silicon, compiler, runtime, and models) all co-engineered to bring high-performance,
Building the Full-Stack AI Future: Chip, Runtime, and Models
For too long, AI hardware and AI research have lived in silos. Hardware vendors chased TOPS and throughput. Model builders
Why AI Chip Makers Need In-House AI Research – Now More Than Ever
Investors sometimes ask: “Why build both the chip and run an AI research team? Isn’t that two businesses?” On the surface,
A continuum of intelligence
SandLogic models span a spectrum - from open-source foundations to fully in-house innovations, each with its own role in the
Shakti LLM Series – Post 2: Built, Not Borrowed
How We Created the Shakti LLMs from Scratch In a world flooded with fine-tuned forks and renamed checkpoints, Shakti was never
Shakti LLM Series – Post 1: Why We Built Sovereign Language Models
After the grand launch of the Shakti LLM Series by Shri Priyank M Kharge, Hon’ble Minister for IT & BT,
Escape the Cloud Tax – Post 7: “What’s Next for EdgeMatrix: Beyond Text, Toward Real-Time Intelligence”
Over the last few posts, we’ve broken down why inference efficiency is non-negotiable when deploying GenAI at scale:📉 Lower token